Credit Risk Analysis Using Machine Learning Models

Business Objective: The project's primary goal is to predict credit defaulters as loan providers find it difficult to give loans due to inconsistencies in credit histories. As a result, the majority of clients accept the risk of defaulting, and loan providers struggle to find the proper customer.

The project highlights that it's critical to check data quality (by excluding redundant variables during the preparation and cleaning phase) and to deal with an imbalanced training dataset to avoid bias in majority class.

We shall also indicate that the features chosen to meet the business objective ( Can we make this decision with only a few features to save time?)   and the algorithm applied to make the decision (whether the borrower defaults or not ) are two important keys in the decision management processing when issuing a loan (here, the bank).